Report
---
title: "S.Deshboard"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: fill
social: ["linkedin","Twitter","Menu"]
source_code: embed
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(dplyr)
library(openintro)
library(highcharter)
library(plotly)
library(ggvis)
library(tidyverse)
```
```{r}
df<-read.csv("C:\\Users\\hp\\OneDrive\\Desktop\\R_dataset\\vehicle.csv")
#str(df)
#View(df)
```
```{r}
col <-c("blue","red","darkgreen","dark","darkorange")
```
interactive data visualization
================================
Row
--------------------------------
### car failure Analysis
```{r}
valueBox(paste("Failure"),
color = "warning")
```
### car failure Analysis in US
```{r}
valueBox(length(df$State),
icon = "fa-user")
```
### **Labour cost **
```{r}
gauge(round(mean(df$lc),
digits = 2),
min =0,
max=350,
gaugeSectors(success = c(0,150),
warning = c(150,240),
danger = c(240,350),
colors = c("green","yellow","red")))
```
### Massachusetts
```{r}
valueBox(sum(df$State=="MA"),
icon = 'fa-building')
```
### California
```{r}
valueBox(sum(df$State=="CA"),
icon = 'fa-building')
```
### Florida
```{r}
valueBox(sum(df$State=="TX"),
icon = 'fa-building')
```
Row
------------------
### Failure by State
```{r}
p1<- df %>%
group_by(State) %>%
summarise(count =n())%>%
plot_ly(x = ~State,
y = ~count,
color = rainbow(51),
type = 'bar')%>%
layout(xaxis = list(title = "Failure by State"),
yaxis =list(title ='Count'))
p1
```
### Top States
```{r}
p2<- df %>%
group_by(State) %>%
summarise(count =n())%>%
filter(count >50)%>%
plot_ly(labels = ~State,
values = ~count,
marker=list(color=rainbow(5)))%>%
add_pie(hole=0.2)%>%
layout(xaxis = list(zeroline= F,
showline=F,
showtricklabel=F,
showgrid=F))
yaxis = list(zeroline= F,
showline=F,
showtricklabel=F,
showgrid=F)
p2
```
### FM Vs Milage
```{r}
p3 <-plot_ly(df,
x = ~fm,
y= ~Mileage,
text=paste("FM:" ,df$fm,"Milage",
df$Mileage),
type = "bar")%>%
layout(xaxis=list(title="FM"),
yaxis=list(title="failure Milage"))
p3
```
### Sctter plot of Month Vs Mileage ->
```{r}
p4 <-plot_ly(df,x= ~fm) %>%
add_markers(y= ~Mileage,
text = ~paste("Mileage:", Mileage),
showlegend =F) %>%
add_lines(y = ~fitted(loess(Mileage ~fm)),
name ="loess Smoother",
color = I("#FFC125"),
showlegend =T,
line =list(width =5))%>%
layout(xaxis = list(title = "month"),
yaxis =list(title = "Mileage"))
p4
```
### Box plot of Top State->
```{r}
df %>%
group_by(State) %>%
ggvis(~State,~lc,fill= ~State) %>%
layer_boxplots()
```
Map
====================
### Map
```{r}
car <- df %>%
group_by(State) %>%
summarise(total =n())
car$State <-abbr2state(car$State)
highchart() %>%
hc_title(text = "car failure in US") %>%
hc_subtitle(text ="source :vehicle.csv")%>%
hc_add_series_map(usgeojson,car,
name="State",
value = "total",
joinBy = c("woename","State")) %>%
hc_mapNavigation(enabled =T)
```
Data Table
=============================
```{r}
datatable(df,caption = "failure Data",
rownames = T,
filter = "top",
options = list(pagelength =25))
```
Pivot Table
============================
```{r}
rpivotTable(df,aggregatorName = "count",
col= "fm",
rows = "state",
rendername = "Heatmap")
```
Summary report { data-orientation =columns}
==========================================
Column{data-Width =100}
------------------------------------------
### Max failure month
```{r}
valueBox(max(df$fm),
icon = "fa user")
```
### Average labour cost
```{r}
valueBox(round(mean(df$lc),
digits = 2),
icon = "fa-area-chart")
```
### Average Mileage at failure
```{r}
valueBox(round(mean(df$Mileage),
digits = 2),
icon = "fa-area-chart")
```
Column
----------------
Report
* This is report on `r length(df$fm)`car failure,
* Average labor cost was `r mean(df$lc)`.
* Average material cost was `r mean(df$mc)`.
* This report was generate on 'r formate(sys.date()),format="%B %d %y)'
About Report
======================
* Created by : Data Scientist at ABC
* confidential : HIGHLY!